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Vision-based approach towards lane line detection and vehicle localization

机译：基于视觉的车道线检测和车辆定位方法

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摘要

Localization of the vehicle with respect to road lanes plays a critical role in the advances of making the vehicle fully autonomous. Vision based road lane line detection provides a feasible and low cost solution as the vehicle pose can be derived from the detection. While good progress has been made, the road lane line detection has remained an open one, given challenging road appearances with shadows, varying lighting conditions, worn-out lane lines etc. In this paper, we propose a more robust vision-based approach with respect to these challenges. The approach incorporates four key steps. Lane line pixels are first pooled with a ridge detector. An effective noise filtering mechanism will next remove noise pixels to a large extent. A modified version of sequential RANdom Sample Consensus) is then adopted in a model fitting procedure to ensure each lane line in the image is captured correctly. Finally, if lane lines on both sides of the road exist, a parallelism reinforcement technique is imposed to improve the model accuracy. The results obtained show that the proposed approach is able to detect the lane lines accurately and at a high success rate compared to current approaches. The model derived from the lane line detection is capable of generating precise and consistent vehicle localization information with respect to road lane lines, including road geometry, vehicle position and orientation.
机译：相对于车道的车辆本地化在使车辆完全自动行驶的过程中起着至关重要的作用。由于可以从检测得出车辆姿态，因此基于视觉的道路车道线检测提供了一种可行的低成本解决方案。尽管取得了良好的进展，但考虑到具有阴影，变化的照明条件，磨损的车道线等具有挑战性的道路外观，道路车道线检测仍然是一个开放的方法。在本文中，我们提出了一种更可靠的基于视觉的方法尊重这些挑战。该方法包括四个关键步骤。车道线像素首先与脊检测器合并。接下来，有效的噪声过滤机制将在很大程度上消除噪声像素。然后在模型拟合过程中采用顺序RANdom样本共识的修改版本，以确保正确捕获图像中的每个车道线。最后，如果道路两侧都存在车道线，则将采用并行度增强技术来提高模型的准确性。获得的结果表明，与当前方法相比，所提出的方法能够准确地并且以高成功率检测车道线。从车道线检测得出的模型能够针对道路车道线生成精确一致的车辆定位信息，包括道路几何形状，车辆位置和方向。

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